Facial recognition artificial intelligence can be deeply flawed, but it's improving all the time, according to Israeli company AnyVision, which creates unique products and algorithms used to control entry to airports, hospitals and casinos and enable state border crossings. Active in 45 countries, AnyVision recently shared its data collection technology at the European Conference on Computer Vision 2020, which ended last week. The company ran a Fair Face Recognition Workshop to see if it was possible to reduce the amount of bias that AI systems reveal. Among the 10 winning teams, the bias was so low it became almost negligible. "In the early days of facial recognition, companies would use bad data," AnyVision researcher Dr. Eduard Vazquez told The Jerusalem Post.

Highly regulated domains such as finance have long favoured the use of machine learning algorithms that are scalable, transparent, robust and yield better performance. One of the most prominent examples of such an algorithm is XGBoost. Meanwhile, there is also a growing interest in building fair and unbiased models in these regulated domains and numerous bias-mitigation algorithms have been proposed to this end. However, most of these bias-mitigation methods are restricted to specific model families such as logistic regression or support vector machine models, thus leaving modelers with a difficult decision of choosing between fairness from the bias-mitigation algorithms and scalability, transparency, performance from algorithms such as XGBoost. We aim to leverage the best of both worlds by proposing a fair variant of XGBoost that enjoys all the advantages of XGBoost, while also matching the levels of fairness from the state-of-the-art bias-mitigation algorithms. Furthermore, the proposed solution requires very little in terms of changes to the original XGBoost library, thus making it easy for adoption. We provide an empirical analysis of our proposed method on standard benchmark datasets used in the fairness community.

Along with the analysis of time-to-event data, it is common to assume that only partial information is given at hand. In the presence of right-censored data with covariates, the conditional Kaplan-Meier estimator (also referred as the Beran estimator) is known to propose a consistent estimate for the lifetimes conditional survival function. However, a necessary condition is the clear knowledge of whether each individual is censored or not, although, this information might be incomplete or even totally absent in practice. We thus propose a study on the Beran estimator when the censoring indicator is not clearly specified. From this, we provide a new estimator for the conditional survival function and establish its asymptotic normality under mild conditions. We further study the supervised learning problem where the conditional survival function is to be predicted with no censorship indicators. To this aim, we investigate various approaches estimating the conditional expectation for the censoring indicator. Along with the theoretical results, we illustrate how the estimators work for small samples by means of a simulation study and show their practical applicability with the analysis of synthetic data and the study of real data for the prognosis of monoclonal gammopathy.

The widespread use of modern machine learning methods in decision making crucially depends on their interpretability or explainability. The human users (decision makers) of machine learning methods are often not only interested in getting accurate predictions or projections. Rather, as a decision-maker, the user also needs a convincing answer (or explanation) to the question of why a particular prediction was delivered. Explainable machine learning might be a legal requirement when used for decision making with an immediate effect on the health of human beings. As an example consider the computer vision of a self-driving car whose predictions are used to decide if to stop the car. We have recently proposed an information-theoretic approach to construct personalized explanations for predictions obtained from ML. This method was model-agnostic and only required some training samples of the model to be explained along with a user feedback signal. This paper uses an information-theoretic measure for the quality of an explanation to learn predictors that are intrinsically explainable to a specific user. Our approach is not restricted to a particular hypothesis space, such as linear maps or shallow decision trees, whose predictor maps are considered as explainable by definition. Rather, we regularize an arbitrary hypothesis space using a personalized measure for the explainability of a particular predictor.

Detroit police wrongfully arrested Robert Julian-Borchak Williams in January 2020 for a shoplifting incident that had taken place two years earlier. Even though Williams had nothing to do with the incident, facial recognition technology used by Michigan State Police "matched" his face with a grainy image obtained from an in-store surveillance video showing another African-American man taking US$3,800 worth of watches. Two weeks later, the case was dismissed at the prosecution's request. However, relying on the faulty match, police had already handcuffed and arrested Williams in front of his family, forced him to provide a mug shot, fingerprints and a sample of his DNA, interrogated him and imprisoned him overnight. Experts suggest that Williams is not alone, and that others have been subjected to similar injustices.

Pakistan has blocked Tinder, Grindr and three other dating apps for not adhering to local laws, its latest move to curb online platforms deemed to be disseminating "immoral content". Pakistan, the second-largest Muslim-majority country in the world after Indonesia, is an Islamic nation where extra-marital relationships and homosexuality are illegal. On Tuesday, the Pakistan Telecommunications Authority said it has sent notices to the management of the five apps, "keeping in view the negative effects of immoral/indecent content streaming". Press Release: PTA has blocked access to five dating/live streaming applications i.e. PTA said the notices issued to Tinder, Grindr, Tagged, Skout and SayHi sought the removal of "dating services" and moderation of live streaming content in accordance with local laws.

Automation in the legal profession will most probably be "a decades-long process" but artificial intelligence (AI) is redefining what it means to be a'great' lawyer. It also means that the market for legal services "will be IT-driven", according to a white paper from Swiss AI consultancy Logol. It used a sports analogy to compare the lawyer of the past to a marathon runner, who after years of training, competes in races relying solely on his muscular strength and endurance. The lawyer of the future, by contrast, "is a race car driver, who, thanks to his car, can move much faster than the marathon runner". It explained: "While physical fitness and endurance are still important characteristics of great race car drivers, pilots also need to understand and master the controls of their car in order to win the race. "The lawyer of tomorrow will no longer have to be the'fastest runner', but the'best driver'; and this will probably hold true for many decades to come." Logol said that recent developments in commercial as well as experimental applications indicated that AI would lead to "unprecedented levels of automation in the legal sector", redefining "the business scenario" and the cost of services, and providing great benefits to the consumer. "However, traditional'human' lawyers are not in danger of disappearing any time soon.

If you're in trouble, would you rather call your lawyer or his AI sidekick? The right answer is not as obvious as one might think. Law is all-encompassing, especially in the business world. Whether we realize it or not, business takes place in the shadow of the law. Law is still, however, one of those fields primarily conducted by humans.

Mia Dand is an instigator. She has created an important platform in AI Ethics that has proven crucial in the times we currently live. Her most recent event, Women in AI Ethics Annual Conference brought together important voices in the current state of diversity in ethics and AI. Founded in 2018, 100 Brilliant Women in AI Ethics (WAIE) list has cultivated an engaged community and has created an emergence of women in research, technology, culture, business – spanning across the globe. What has emanated are the stories and lessons from their important works that have spilled into the mainstream.

Traditional colonial power seeks unilateral power and domination over colonised people. It declares control of the social, economic, and political sphere by reordering and reinventing the social order in a manner that benefits it. In the age of algorithms, this control and domination occurs not through brute physical force but rather through invisible and nuanced mechanisms such as control of digital ecosystems and infrastructure. Common to both traditional and algorithmic colonialism is the desire to dominate, monitor, and influence the social, political, and cultural discourse through the control of core communication and infrastructure mediums. While traditional colonialism is often spearheaded by political and government forces, digital colonialism is driven by corporate tech monopolies--both of which are in search of wealth accumulation. The line between these forces is fuzzy as they intermesh and depend on one another. Political, economic, and ideological domination in the age of AI takes the form of "technological innovation", "state-of-the-art algorithms", and "AI solutions" to social problems. Algorithmic colonialism, driven by profit maximisation at any cost, assumes that the human soul, behaviour, and action is raw material free for the taking.